$118.31
Only 1 left in stock.
US imports may differ from local products. Additional terms apply. Learn More.

Amazon Global Store

  • International products have separate terms and are sold from abroad and may differ from local products including fit, age rating, and language of product, labeling, or instructions, or plugs (you may require an adapter).
  • Manufacturer warranty may not apply but you may have other rights under law.
  • Learn more about Amazon Global Store
$$118.31 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$118.31
Subtotal
Initial payment breakdown
Delivery cost, delivery date and order total (including tax) shown at checkout.
Ships from
Amazon US
Ships from
Amazon US
Sold by
Amazon US
Sold by
Amazon US
Payment
Secure transaction
Your transaction is secure
We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more
Payment
Secure transaction
We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more
Kindle app logo image

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet or computer—no Kindle device required.

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera, scan the code below and download the Kindle app.

QR code to download the Kindle App

Follow the author

Aurélien Géron
+ Follow
More books from this author
Something went wrong. Please try your request again later.

Hands–On Machine Learning with Scikit–Learn and TensorFlow Paperback – 24 March 2017

by Aurelien Geron (Author)
4.6 4.6 out of 5 stars 1,309 ratings
Edition: 1st
See all formats and editions
{"desktop_buybox_group_1":[{"displayPrice":"$118.31","priceAmount":118.31,"currencySymbol":"$","integerValue":"118","decimalSeparator":".","fractionalValue":"31","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"v50SrJ4G%2BLc0Wdr5YuevcCKobXwhwmRYnAvw%2Fx8yU2yFv%2Fe9Gtr9SpEP9J0NqbKf082YjauR0n4NUkzTZ2g7giDBJNvVHvLgrH26vxW6MzKX7CAb7I8oUYC%2FSaeUNP8e%2FtmpZTbXr06N8neJeMbleLGoNo6Xn0OAdvT6YC3y8z52IchYh9YLYEOVlRawhRvx","locale":"en-AU","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}]}

Purchase options and add-ons

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Read more Read less
Previous page
  1. ISBN-10
    1491962291
  2. ISBN-13
    978-1491962299
  3. Edition
    1st
  4. Publisher
    O′Reilly
  5. Publication date
    24 March 2017
  6. Language
    English
  7. Dimensions
    18 x 2.89 x 23.3 cm
  8. Print length
    566 pages
  9. See all details
Next page

Frequently bought together

Hands–On Machine Learning with Scikit–Learn and TensorFlow
$118.31
Get it 22 - 30 May
Only 1 left in stock.
Ships from and sold by Amazon US.
+
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
$78.33
In stock
Ships from and sold by Amazon AU.
+
Deep Learning
$40.95
Get it 29 May - Jun 5
In stock
Ships from and sold by World Book collections.
Total Price:
To see our price, add these items to your cart.
Details
Added to Cart
Some of these items dispatch sooner than the others.
Show details Hide details
Choose items to buy together.

From the Publisher


Prerequisites

This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.

Also, if you care about what’s under the hood you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).

More about this book

Machine Learning in Your Projects

Naturally you are excited about Machine Learning and you would love to join the party!

Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look.

For example:

  • Segment customers and find the best marketing strategy for each group
  • Recommend products for each client based on what similar clients bought
  • Detect which transactions are likely to be fraudulent
  • Predict next year’s revenue
  • And more!
Objective and Approach

This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data.

We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions.

Rather than implementing our own toy versions of each algorithm, we will be using actual production-ready Python frameworks:

Scikit-Learn

Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning.

TensorFlow

TensorFlow is a more complex library for distributed numerical computation using data flow graphs. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially thousands of multi-GPU servers. TensorFlow was created at Google and supports many of their large-scale Machine Learning applications. It was open-sourced in November 2015.

Product description

About the Author

Aurelien Geron has worked as a software engineer for a consulting firm in Paris, an IoT startup in Montreal (back in 1999!), and has also worked as co-founder and CTO of a leading wireless ISP in France (Wifirst). He was the product manager for YouTube's video classification team.He has authored a WiFi book, a C++ book, and taught CS in French engineering schools. A few personal fun facts: Aurelien grew up in France, Nigeria, New Zealand, and the U.S. (Berkeley). He studied microbiology and evolutionary genetics before going into software engineering. He was the singer in a rock band, has 2 turtles and 3 hens, has scuba dived with 10-foot sharks, taught his 5-year-old son to count in binary on his fingers (up to 1023), and his parachute didn't open on the 2nd jump.

Product details

  • Publisher ‏ : ‎ O′Reilly; 1st edition (24 March 2017)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 566 pages
  • ISBN-10 ‏ : ‎ 1491962291
  • ISBN-13 ‏ : ‎ 978-1491962299
  • Dimensions ‏ : ‎ 18 x 2.89 x 23.3 cm
  • Customer Reviews:
    4.6 4.6 out of 5 stars 1,309 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.
Aurélien Géron

Aurélien Géron

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.

Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.

Customer reviews

4.6 out of 5 stars
4.6 out of 5
1,309 global ratings

Review this product

Share your thoughts with other customers
Write a customer review

Top reviews from Australia

eric yuen
5.0 out of 5 stars Great book that explains the what and how to in Machine Learning
Reviewed in Australia on 12 April 2019
Verified Purchase
With an interest to understand what machine learning is, it appears quite intimidate and hard to find a book that is easy to read and without the heavy math (I am at an age of close to retirement, so my math in calculus is clearly rusty). This book just the right choice as it is easy to read without the burden of equations after equations, and has provided a roadmap leading to all relevant topics with great length of details that are clearly articulated. I particular like chapter 2, which gives an idea of what an end-to-end project is like, right at the early part of the book. The examples included of how to use the Scikit-Learn library really pump me into action.
Helpful
 Report
Tendou
5.0 out of 5 stars A must-read for those who are new to machine learning
Reviewed in Australia on 19 October 2019
Verified Purchase
Very good book in general. Clear explanations and codes. The language is also very friendly and easy to understand for those who do not have data science/ computer science background like myself.
Helpful
 Report
Andrew Hazon
4.0 out of 5 stars Four Stars
Reviewed in Australia on 7 June 2018
Verified Purchase
Good book to get started in ML
Helpful
 Report

Top reviews from other countries

Translate all reviews to English
lmoliveira
5.0 out of 5 stars Machine Learning - The best book.
Reviewed in Brazil on 7 February 2019
Verified Purchase
This is an amazing book. It doesn't matter whether you are starting with Data Science or if you are just improving your knowledge. The author explains in a direct, and simple way, as far as it's possible, some tough subjects. Probably it's one of the best books in the area.
One person found this helpful
 Report
Ruben
5.0 out of 5 stars Indispensable para quienes buscan aprender Machine Learning.
Reviewed in Mexico on 24 February 2018
Verified Purchase
Excelente libro, para quienes están empezando y para quienes tienen cierta experiencia en este campo.

- Utiliza herramientas actuales y las librerías mas usadas.
- Aplicaciones reales con datos reales.
- Referencias a sitios web relacionados con el tema.
- Ejercicios muy interesantes y actuales.
- Conceptos muy bien explicados.

En lo personal poseo cierta experiencia en estos temas y no esperaba mucho de este libro, pero al tenerlo y empezar a leerlo me fascino, un libro mus imágenes.y bien hecho y se nota desde las primeras paginas que el autor es un experto en el tema, las herramientas y los ejemplos son muy y repito muy prácticos, fácilmente puedes replicar el código de ejemplo para tus necesidades y tus propias aplicaciones de ML.

Un Excelente libro, me atrevería a decir que de los mejores en la actualidad.
Altamente Recomendable.
Report
Miguel Angel Salinas Gancedo
5.0 out of 5 stars Muy completo
Reviewed in Spain on 10 October 2019
Verified Purchase
Para mi el mejor libro de Machine Learning, mu completo y con muy bueno ejemplos que van más haya de los típicos en otros libros.
Report
Mauri Claudio
5.0 out of 5 stars A must-have book for any machine learning practitioner.
Reviewed in Italy on 9 August 2019
Verified Purchase
Excellent text. Covers both the theory and the practice of modern machine learning, providing the reader with a solid background , needed to tackle the matter with confidence.
Report
Dr. Chrilly Donninger
5.0 out of 5 stars Hätte auch 6 Sterne verdient.
Reviewed in Germany on 12 February 2019
Verified Purchase
Ich war bis vor Kurzem der Meinung, dass sich ein Real-Programmer nicht mit so etwas wie Python die Hände schmutzig machen sollte. In Vorbereitung für ein Projekt habe ich es mir doch näher angeschaut. Über die Sprache kann man diskutieren, aber die Bibliotheken sind wirklich brauchbar und offensichtlich auch sehr effizient implementiert (es werden good old Fortran und C Bibliotheken aufgerufen).
Das Buch bietet eine ausgezeichnete Einführung in die beiden wichtigsten Statistik-Bibliotheken scikit-learn und Tensorflow. Besonders beeindruckt hat mich Kapitel 2. Es wird ein Beispiel - die Prognose von Immobilienpreisen in Kalifornien - von A-Z genau präsentiert. Man lernt auch die mundanen aber in der Praxis sehr kritischen Dinge des Statistiker-Lebens. Wie schaut man sich die Daten möglichst anschaulich an, wie reinigt man sie, beseitigt missing-values ... So etwas habe ich in diesem Detail noch nie in einem Statistik-Lehrbuch gefunden.
Es werden neben dem praktischen Kode im gesamten Buch aber auch die wichtigsten statistischen Eigenschaften besprochen, der Autor diskutiert das Verhalten von unterschiedlichen Optimierungsstrategien von Tensorflow ...
Es bleiben natürlich immer auch Wünsche übrig. Ich hätte mir noch etwas mehr zum Thema Time-Series und Neural Networks gewünscht. Auch auf das keras package hätte der Autor etwas detaillierter eingehen können. Das ist offensichtlich geplant. Es gibt bereits die Ankündigung einer neuen Auflage für Juni 2019. Der Titel ist um "keras" erweitert.
Eine gute Ergänzung zu diesem Buch ist Jake VanderPlas: Python Data Science Handbook. Mit diesen beiden Büchern erhält man eine solide Grundlage für das Gebiet. Man muss dann "nur noch" selber was machen und im echten Projektleben Erfahrung sammeln.
14 people found this helpful
 Report